New trend definitions and scalable solutions for social networks

Social networks provide a large-scale information infrastructure for people to discuss and exchange ideas about variety of topics. Detecting trends of such topics is of signiﬁcant interest for many reasons. For one, it can be used to detect emergent behavior in the network, for instance a sudden increase in the number of people talking about explosives or biological warfare. Information trends can also be viewed as a reﬂection of societal concerns or even as a consensus of collective decision making. Understanding how a community decides that a topic is trendy can help us better understand how ad-hoc communities are formed and how decisions are made in such communities. In general, constructing “useful” trend deﬁnitions and providing scalable solutions that detect such trends will contribute towards a better understanding of human interactions in the context of social media.

As part of an ongoing project, we investigate scalable solutions for various trend deﬁnitions that incorporate various important aspects such as the spatiotemporal dimensions of human interaction and the network structure. More precisely, we study trends from two diﬀerent perspectives; ontology-based trends analysis that depends on features such as temporal and spatial properties of the content that is broadcast and structural trend analysis that depend on structural connections between the users who are broadcasting. Our ultimate goal is to develop a ﬂexible solution that incorporates all such aspects into one framework.